Supervised Machine Learning: Optimization Framework and Applications with SAS and R

AI framework addresses bias-variance tradeoff for supervised learning methods in real-life applications. It uses bootstrapping, statistical experiments, and data contamination to build robust classifiers. The framework is available on GitHub.

\n Format: Hardback
\n Length: 160 pages
\n Publication date: 22 September 2020
\n Publisher: Taylor & Francis Ltd
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Supervised learning methods in real-life applications often face a tradeoff between bias and variance. To address this challenge, a novel AI framework has been developed. This framework encompasses several key features, including:

Bootstrapping: The process of creating multiple training and testing data sets with varying characteristics is known as bootstrapping. This helps in dealing with bias, as it allows for a more comprehensive evaluation of the classifiers' performance.

Design and Analysis of Statistical Experiments: Statistical experiments are used to identify optimal feature subsets and hyper-parameters for ML methods. By analyzing the data, researchers can determine the features that contribute most to the model's accuracy and improve the overall performance.

Data Contamination: Data contamination is a technique used to test the robustness of the classifiers. It involves introducing artificially generated data into the training set to assess whether the classifiers can still accurately classify the new data.

The AI framework is designed to be a table-driven environment, allowing for easy management of all meta-data related to the proposed framework. It also provides interoperability with R libraries, enabling a wide range of statistical and machine-learning tasks to be accomplished.

To facilitate the development and usage of the AI framework, computer programs in R and SAS have been made available on GitHub. These programs enable researchers to create and customize the framework to suit their specific needs.

By leveraging bootstrapping, statistical experiments, and data contamination, the AI framework aims to improve the accuracy and generalization of supervised learning methods in real-life applications. Its innovative approach and comprehensive features make it a valuable tool for researchers and practitioners in the field of AI.

\n Weight: 420g\n
Dimension: 162 x 241 x 17 (mm)\n
ISBN-13: 9780367277321\n \n